ALwrity Version 0.5.0 (Fastapi + React )
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ajaysi
232
backend/services/llm_providers/gemini_provider.py
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232
backend/services/llm_providers/gemini_provider.py
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# Using Gemini Pro LLM model
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import os
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import sys
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from pathlib import Path
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import google.genai as genai
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from google.genai import types
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from dotenv import load_dotenv
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load_dotenv(Path('../../../.env'))
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from loguru import logger
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logger.remove()
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logger.add(sys.stdout,
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colorize=True,
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format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
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)
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_random_exponential,
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)
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import asyncio
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import json
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import re
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# Configure standard logging
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import logging
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logging.basicConfig(level=logging.INFO, format='[%(asctime)s-%(levelname)s-%(module)s-%(lineno)d]- %(message)s')
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logger = logging.getLogger(__name__)
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@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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def gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_prompt):
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""" Common functiont to get response from gemini pro Text. """
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#FIXME: Include : https://github.com/google-gemini/cookbook/blob/main/quickstarts/rest/System_instructions_REST.ipynb
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try:
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client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
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except Exception as err:
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logger.error(f"Failed to configure Gemini: {err}")
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logger.info(f"Temp: {temperature}, MaxTokens: {max_tokens}, TopP: {top_p}, N: {n}")
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# Set up AI model config
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generation_config = {
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"temperature": temperature,
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"top_p": top_p,
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"top_k": n,
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"max_output_tokens": max_tokens,
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}
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# FIXME: Expose model_name in main_config
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try:
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response = client.models.generate_content(
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model='gemini-2.5-pro',
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contents=prompt,
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config=types.GenerateContentConfig(
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system_instruction=system_prompt,
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max_output_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=n,
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),
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)
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#logger.info(f"Number of Token in Prompt Sent: {model.count_tokens(prompt)}")
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return response.text
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except Exception as err:
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logger.error(f"Failed to get response from Gemini: {err}. Retrying.")
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#@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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#def gemini_blog_metadata_json(blog_content):
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# """ Common functiont to get response from gemini pro Text. """
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# prompt = f"I will provide you with the content of a blog post. Based on this content, you need to generate the following elements in JSON format:\n\n1. **Blog Title**: A compelling and relevant title that summarizes the blog content.\n2. **Meta Description**: A concise meta description (up to 160 characters) that captures the essence of the blog post and encourages clicks.\n3. **Tags**: A list of 5-10 relevant tags that represent the key topics covered in the blog post.\n4. **Categories**: A list of 1-3 appropriate categories that best describe the blog post's main themes.\n\nOutput your response in the following JSON format:\n\n```json\n{\n \"type\": \"object\",\n \"properties\": {\n \"blog_title\": {\n \"type\": \"string\"\n },\n \"meta_description\": {\n \"type\": \"string\"\n },\n \"tags\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"categories\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n }\n }\n}\n\n. The Blog Content is given below: \n\n{blog_content}\n\n"
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#
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# try:
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# genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
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# except Exception as err:
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# logger.error(f"Failed to configure Gemini: {err}")
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#
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# # Create the model
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# generation_config = {
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# "temperature": 1,
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# "top_p": 0.95,
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# "top_k": 64,
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# "max_output_tokens": 8192,
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# "response_schema": content.Schema(
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# type = content.Type.OBJECT,
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# properties = {
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# "response": content.Schema(
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# type = content.Type.STRING,
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# ),
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# },
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# ),
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# "response_mime_type": "application/json",
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# }
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#
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# model = genai.GenerativeModel(
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# model_name="gemini-1.5-flash",
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# generation_config=generation_config,
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# # safety_settings = Adjust safety settings
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# # See https://ai.google.dev/gemini-api/docs/safety-settings
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# )
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#
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# try:
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# # text_response = []
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# response = model.generate_content(prompt)
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# if response:
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# logger.info(f"Number of Token in Prompt Sent: {model.count_tokens(prompt)}")
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# return response.text
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# except Exception as err:
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# logger.error(f"Failed to get SEO METADATA from Gemini: {err}. Retrying.")
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async def test_gemini_api_key(api_key: str) -> tuple[bool, str]:
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"""
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Test if the provided Gemini API key is valid.
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Args:
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api_key (str): The Gemini API key to test
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Returns:
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tuple[bool, str]: A tuple containing (is_valid, message)
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"""
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try:
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# Configure Gemini with the provided key
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genai.configure(api_key=api_key)
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# Try to list models as a simple API test
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models = genai.list_models()
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# Check if Gemini Pro is available
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if any(model.name == "gemini-pro" for model in models):
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return True, "Gemini API key is valid"
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else:
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return False, "Gemini Pro model not available with this API key"
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except Exception as e:
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return False, f"Error testing Gemini API key: {str(e)}"
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def gemini_pro_text_gen(prompt, temperature=0.7, top_p=0.9, top_k=40, max_tokens=2048):
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"""
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Generate text using Google's Gemini Pro model.
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Args:
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prompt (str): The input text to generate completion for
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temperature (float, optional): Controls randomness. Defaults to 0.7
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top_p (float, optional): Controls diversity. Defaults to 0.9
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top_k (int, optional): Controls vocabulary size. Defaults to 40
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max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 2048
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Returns:
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str: The generated text completion
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"""
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try:
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# Configure the model
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model = genai.GenerativeModel('gemini-pro')
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# Generate content
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response = model.generate_content(
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prompt,
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generation_config=genai.types.GenerationConfig(
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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max_output_tokens=max_tokens,
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)
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)
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# Return the generated text
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return response.text
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except Exception as e:
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logger.error(f"Error in Gemini Pro text generation: {e}")
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return str(e)
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def gemini_structured_json_response(prompt, schema, temperature=0.7, top_p=0.9, top_k=40, max_tokens=2048, system_prompt=None):
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"""
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Generate structured JSON response using Google's Gemini Pro model.
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Args:
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prompt (str): The input text to generate completion for
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schema (dict): The JSON schema to follow for the response
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temperature (float, optional): Controls randomness. Defaults to 0.7
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top_p (float, optional): Controls diversity. Defaults to 0.9
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top_k (int, optional): Controls vocabulary size. Defaults to 40
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max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 2048
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system_prompt (str, optional): System instructions for the model
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Returns:
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dict: The generated structured JSON response
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"""
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try:
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# Configure the model
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client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
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# Set up generation config
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generation_config = {
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"temperature": temperature,
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"top_p": top_p,
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"top_k": top_k,
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"max_output_tokens": max_tokens,
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}
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# Generate content with structured response
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response = client.models.generate_content(
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model='gemini-2.5-pro',
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contents=prompt,
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config=types.GenerateContentConfig(
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system_instruction=system_prompt,
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max_output_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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response_mime_type='application/json',
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response_schema=schema
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),
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)
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# Parse the response
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try:
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# First try to get the parsed response
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if hasattr(response, 'parsed'):
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return response.parsed
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# If parsed is not available, try to parse the text
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response_text = response.text
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return json.loads(response_text)
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except json.JSONDecodeError as e:
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logger.error(f"Error parsing JSON response: {e}")
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return {"error": f"Failed to parse JSON response: {e}", "raw_response": response_text}
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except Exception as e:
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logger.error(f"Error in Gemini Pro structured JSON generation: {e}")
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return {"error": str(e)}
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